Quantification of Components in Non-Homogenous Pharmaceutical Tablets Using near Infrared Reflectance Imaging

Abstract

The adequacy of quantification of the components in non-homogeneous pharmaceutical tablets using near infrared (NIR) linear hyperspectral unmixing has been studied with and without the presence of the tablet coating and an inefficient blending process. NIR images of six coated tablets of different formulations and of sections thereof, extracted at specific depths, were acquired. The quantification of the compounds present was performed via the classical least squares criterion. The results presented show that given the low depth of penetration of the NIR radiation, the coating represents as much as 30% of the mass fraction of the sampled tablet, even though it is commonly less than 3% of the mass fraction of the total tablet. This deviation negatively impacts the estimates of the compounds of interest. Estimates of the compounds obtained in sections approached the true values and were consistent at each depth. Both scenarios (coated and uncoated) were found to be well explained by a linear mixing model, as the errors in spectral estimates were small. The pixels showing the highest fraction of energy not explained by the linear model were found to be spatially located in areas where the surface curvature was high and a high reflectivity was observed; factors known to contribute non-linearities to the mixing scenario. The outcomes of this study have practical importance for the pharmaceutical industry: first, tablets should always be uncoated or microtomed before being imaged, in order to remove the high contribution of the coating and to obtain a flat surface. These steps reduce the above-mentioned non-linearities that introduce errors when using a linear mixing model. Second, the mixing scale shall be taken into account, as a poor blending may not show a similar in-depth pattern and thus provide different estimates at each depth and, on the other hand, over-blending may lead to a non-linear mixing scenario, depending on the spatial resolution used. An appropriate prior study will direct analysis either to linear or non-linear approaches.